Multi-satellite cooperative resource scheduling method for air target space search missions

Abstract

This invention provides a multi-satellite collaborative resource scheduling method for airborne target space search missions, relating to the field of satellite observation resource scheduling technology. The method employs a 3D mesh vertex method to generate satellite observation strips, comprehensively considering the three-dimensional maneuvering characteristics of airborne targets. This extends the search mission from a two-dimensional plane to a three-dimensional space and avoids complex direct calculations of spatial geometry. This enables the search to effectively cover targets at different altitudes, solving the problem that traditional two-dimensional scheduling methods cannot reliably detect airborne targets due to neglecting the altitude dimension.

Description

Technical Field

[0001] This invention relates to the field of satellite observation resource scheduling technology, specifically to a multi-satellite collaborative resource scheduling method for space search missions targeting aerial targets. Background Technology

[0002] In recent years, the space industry has continued to develop rapidly, with the number and scale of satellites in orbit constantly expanding, leading to a growing demand for satellite observation. However, ground observation resources are relatively limited, and the contradiction of "too many satellites and too few stations" is becoming increasingly prominent, further exacerbating the complexity of solving the observation scheduling problem. Therefore, how to efficiently utilize limited ground observation resources and achieve reasonable and optimized satellite observation scheduling has become an important research direction.

[0003] Existing research mainly includes: classifying ground observation tasks into ordinary point targets (coverable in a single observation), periodic point targets (requiring specified observation frequency), and regional targets (covered by multi-satellite planar strip stitching); designing differentiated observation strategies for different task types, such as prioritizing matching point targets with satellites that have more visible windows, and decomposing regional targets into sub-targets according to swath width and then allocating them to multiple satellites; constructing a generalized observation benefit function to uniformly quantify the "single observation benefit" of point targets and the "unit area benefit" of regional targets, solving the problem of prioritizing different types of tasks; and adopting the DRL-ALNS algorithm, which uses deep reinforcement learning (DRL) to autonomously select the destruction / repair operators and parameters (destruction degree, simulated annealing temperature) of ALNS to cope with the search space challenges of large-scale satellite constellations and improve scheduling efficiency and benefits.

[0004] However, existing research is designed specifically for ground target observation missions and cannot characterize and process the distribution and maneuvering characteristics of aerial targets in the altitude dimension. This leads to the problem of unreliable detection of aerial targets due to the neglect of the altitude dimension. Therefore, it is fundamentally unsuitable for satellite resource scheduling problems that require collaborative search of three-dimensional airspace containing latitude, longitude, and altitude information. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a multi-satellite collaborative resource scheduling method for airborne target space search missions, which solves the technical problem that existing multi-satellite collaborative resource scheduling methods cannot reliably detect airborne targets due to ignoring the altitude dimension.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a multi-satellite cooperative resource scheduling method for space search missions targeting aerial targets, comprising: Acquire relevant data on multi-satellite collaborative resources for space search missions targeting aerial targets; Based on relevant data from multi-satellite collaborative resources, satellite observation strips are generated using the 3D grid vertex method. Select the dominant satellite observation strips from the satellite observation strips; Based on the relevant data of multi-satellite collaborative resources and the dominant candidate observation strips, a three-dimensional spatial search mission planning model for airborne targets is constructed. The satellite scheduling scheme is obtained by solving the three-dimensional spatial search mission planning model for aerial targets; The method of generating satellite observation strips using a 3D mesh vertex method includes: Determine the satellite In orbital orbit Potential space for aerial targets Internal 3D mesh vertices Starting from the vertex, the field of view is expanded to one side to form a corresponding angular range; Starting with the visible time of the vertex, the time is extended according to the maximum single observation duration to form the corresponding time interval [visible time, visible time + maximum single observation duration], and the corresponding maximum coverage area is determined based on the time interval; space For all other vertices within the range, check their lateral angle and visibility time to see if they fall within the maximum coverage area. Merge the time windows of vertices that fall within the maximum coverage area to form a time window set, thus creating a potential space for aerial targets. In satellite track The observation strips within.

[0007] Preferably, the step of selecting the dominant satellite observation strip from the satellite observation strips includes: against ,in, It is the potential space for aerial targets. In satellite track The set of observation strips within, ; If the observed strip With observation strips If the former's coverage grid set contains the latter's coverage grid set, and the former's observation time range falls within the latter's observation time range, then the former is dominant over the latter. Remove the dominant observation bands .

[0008] Preferably, the planning model for the three-dimensional spatial search mission of aerial targets includes an objective function and constraints; The objective function aims to maximize both the task profitability and the energy surplus rate, and its expression is as follows: (1) In the formula, The constraints include equations (2), (3), and (4): The observation time for each observation strip cannot exceed the maximum single observation duration of the satellite: (2) Two observation strips on the same satellite with time conflicts cannot be executed simultaneously: (3) The total energy consumption for observations within each satellite orbital revolution must not exceed the maximum energy consumption for that revolution. (4) (5) The range of values ​​for the decision variable: (6) in, For the reason The set of three-dimensional grids corresponding to a given observation strip; , It is a collection of satellites. It is the total number of satellites; , It is a satellite The set of orbital cycles within a scheduling period. For satellites within the scheduling cycle Total number of laps; satellite Maximum energy within a single orbital cycle; satellite The lateral sway velocity; satellite The energy consumed per unit observation time; satellite The maximum duration of a single observation; , It is a potential spatial collection of aerial targets. It represents the total potential space for aerial targets; It is the potential space for aerial targets. The benefits; It is the potential space for aerial targets. Discrete 3D mesh set; Potential space for aerial targets The proportion of the total number of observation grids to the total number of discrete 3D grids; It is the weight of the task's rate of return; It is the weight of the energy surplus rate; , It is the potential space for aerial targets. In satellite track Inner One observation band, It is the start time of the observation. It is the end time of the observation. It is the lateral sway angle of the observation strip. It is the set of three-dimensional grids covered by the strip; , It is the potential space for aerial targets. In satellite track The set of observation strips within, The number of its observed bands; It is a satellite The set of conflicting pairs of the upper observation strip; Indicates the potential space of aerial targets In satellite track Inner Whether an observation strip is executed is determined by a 1 if executed, otherwise by a 0.

[0009] Preferably, solving the three-dimensional spatial search task planning model for aerial targets includes: Set the algorithm input data and execution parameters, randomly generate an initial solution, and determine the current optimal solution based on the initial solution. As the globally optimal solution; where the input data includes the optimal strip set. The objective function and constraints of the three-dimensional spatial search mission planning model for aerial targets; execution parameters include initial temperature, cooling coefficient, termination temperature, taboo table length, and taboo search iteration number, etc. Determine if the current temperature is greater than the termination temperature. If so, execute the simulated annealing stage. Update the optimal solution by comparing the objective function value and the adaptive acceptance criterion until the current temperature is less than or equal to the termination temperature. Output the updated global optimal solution as the simulated annealing optimal solution. Using the optimal solution from simulated annealing as the initial baseline solution, a tabu search is performed to output the final global optimal solution; Based on the stripes with an execution state of 1 in the final global optimal solution, organize and output the execution schemes of each satellite as the satellite scheduling scheme.

[0010] Preferably, the simulated annealing phase includes: For the current optimal solution Randomly select different "satellite-orbit-region" strips and flip their decision variables. Generate neighborhood solutions Constraint verification logic is embedded synchronously during the neighborhood solution generation process to verify the generated solution. The system performs real-time checks to ensure that constraints are met. If a constraint is not met, the system is immediately regenerated until a result that meets the constraints is obtained. ; Calculate the current optimal solution respectively objective function value and neighborhood solutions objective function value ; like Accept directly ,renew ; like Set an acceptance probability P and generate random numbers. ,like Then accept ,renew ; like ,renew Otherwise, keep constant; Update current temperature , ; The cooling coefficient; After the phase ends, output the optimal solution for simulated annealing. .

[0011] Preferably, the calculation method for the acceptance probability P includes: in, This represents the coefficient of difference between the current solution and the objective function of the global optimal solution.

[0012] Preferably, the tabu search includes: Initialize the taboo table Empty; The optimal solution output by the simulated annealing stage As the initial baseline solution for the tabu search, we prioritize retaining the strips with higher objective functions and select strips with lower objective functions for inversion. Generate neighborhood solutions that meet the constraints. ; Extract the strip flipped combination of each neighborhood solution; if not... Then the middle is included in the candidate solution set. ;calculate The objective function of each solution is used to select the solution with the highest profit. ; like , lift the ban The flipped combination, updated and add combinations of other candidate solutions. ; like , directly Combination ; like Delete the earliest added combination, among which, This is the maximum length of the taboo list; After the phase ends, output As the final globally optimal solution.

[0013] Secondly, the present invention provides a multi-satellite collaborative resource scheduling system for air target space search missions, wherein the multi-satellite collaborative resource scheduling system for air target space search missions is used to execute the multi-satellite collaborative resource scheduling method for air target space search missions as described above.

[0014] Thirdly, the present invention provides a computer-readable storage medium storing a computer program for multi-satellite cooperative resource scheduling for an airborne target space search mission, wherein the computer program causes a computer to execute the multi-satellite cooperative resource scheduling method for an airborne target space search mission as described above.

[0015] Fourthly, the present invention provides an electronic device, comprising: One or more processors; Memory; and One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a multi-satellite cooperative resource scheduling method for performing an airborne target space search mission as described above.

[0016] (III) Beneficial Effects This invention provides a multi-satellite cooperative resource scheduling method for space search missions targeting aerial targets. Compared with existing technologies, it has the following advantages: This invention employs a 3D mesh vertex method to generate satellite observation strips, comprehensively considering the three-dimensional maneuvering characteristics of aerial targets. It extends the search task from a two-dimensional plane to a three-dimensional space and avoids direct calculation of complex spatial geometry. This enables the search to effectively cover targets at different altitudes, solving the problem that traditional two-dimensional scheduling methods cannot reliably detect aerial targets due to ignoring the altitude dimension. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A comparison chart of two-dimensional planar search and three-dimensional spatial search; Figure 2 This is a block diagram of a multi-satellite collaborative resource scheduling method for an airborne target space search mission, according to an embodiment of the present invention. Figure 3 This is a diagram illustrating the stripe generation process. Figure 4 This is a flowchart illustrating how to solve a three-dimensional spatial search mission planning model for aerial targets using an improved simulated annealing algorithm. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This application provides a multi-satellite collaborative resource scheduling method for airborne target space search missions, which solves the technical problem that existing multi-satellite collaborative resource scheduling methods cannot reliably detect airborne targets due to ignoring the altitude dimension. It expands the search space from the traditional two-dimensional plane to a three-dimensional space, enabling the search to effectively cover targets at different altitude levels.

[0021] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows: The purpose of this invention is to conduct a comprehensive three-dimensional search of potential airborne targets using multi-satellite observation capabilities, avoiding the problem of inconsistent coverage at higher altitudes in adjacent satellite observation strips resulting from two-dimensional planar searches. To address this practical observation requirement, this invention proposes a multi-satellite collaborative resource scheduling method for airborne target space search tasks. A comparison diagram of two-dimensional planar search and three-dimensional space search is shown below. Figure 1As shown, firstly, based on the discretization method of three-dimensional space, the potential space of airborne targets is discretized into multiple three-dimensional grids. A satellite observation strip generation algorithm based on the vertices of the three-dimensional grids is designed to form candidate observation strips of satellites without losing the opportunity for the optimal solution. Then, a dominance criterion for satellite observation strips is constructed to eliminate a large number of invalid candidate observation strips, reducing the solution space of the multi-satellite collaborative scheduling problem for airborne target space search tasks. Furthermore, a three-dimensional space search task planning model with the goal of maximizing observation yield and energy surplus rate is established. This model comprehensively considers multiple constraints such as the maximum single observation duration, observation strip conflicts, and the upper limit of orbital orbit energy. An improved simulated annealing algorithm is designed, which enhances the algorithm's wide-area exploration capability through the combination of various neighborhood search operators, thereby effectively improving the quality of the solution and the convergence speed of the algorithm, and finally realizing the search efficiency of multiple satellites collaboratively searching the potential space of airborne targets.

[0022] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0023] This invention provides a multi-satellite cooperative resource scheduling method for space search missions targeting aerial targets, such as... Figure 2 As shown, it includes: S1. Acquire relevant data on multi-satellite collaborative resources for space search missions targeting aerial targets; S2. Based on the relevant data of multi-satellite collaborative resources, satellite observation strips are generated using the three-dimensional grid vertex method; S3. Select the dominant satellite observation strips from the satellite observation strips; S4. Based on the relevant data of multi-satellite collaborative resources and the dominant candidate observation strips, construct a three-dimensional spatial search mission planning model for airborne targets; S5. Solve the three-dimensional spatial search mission planning model for aerial targets to obtain the satellite scheduling scheme; The method of generating satellite observation strips using a 3D mesh vertex method includes: Determine the satellite In orbital orbit Potential space for aerial targets Internal 3D mesh vertices Starting from the vertex, the field of view is expanded to one side to form a corresponding angular range; Starting with the visible time of the vertex, the time is extended according to the maximum single observation duration to form the corresponding time interval [visible time, visible time + maximum single observation duration], and the corresponding maximum coverage area is determined based on the time interval; space For all other vertices within the range, check their lateral angle and visibility time to see if they fall within the maximum coverage area. Merge the time windows of vertices that fall within the maximum coverage area to form a time window set, thus creating a potential space for aerial targets. In satellite track The observation strips within.

[0024] The present invention employs a three-dimensional mesh vertex method to generate satellite observation strips, which comprehensively considers the three-dimensional maneuvering characteristics of aerial targets, extending the search task from a two-dimensional plane to a three-dimensional space, and avoiding direct calculation of complex spatial geometry. This enables the search to effectively cover targets at different altitude levels, solving the problem that traditional two-dimensional scheduling methods cannot reliably detect aerial targets due to ignoring the altitude dimension.

[0025] In step S1, relevant data on multi-satellite collaborative resources for space search missions targeting aerial targets are acquired. The specific implementation process is as follows: The relevant data includes the satellite set, the set of satellite orbital cycles in the scheduling cycle, the satellite's yaw rate, and the maximum single observation duration of the satellite.

[0026] To facilitate subsequent explanations, the definitions of relevant data and some parameters that will appear later are explained in detail below: , It is a collection of satellites. It is the total number of satellites; , It is a satellite The set of orbital cycles within a scheduling period. For satellites within the scheduling cycle Total number of laps; It is a satellite Maximum energy within a single orbital cycle; It is a satellite Lateral yaw rate (unit: degrees / second); It is a satellite The energy consumed per unit observation time; It is a satellite The maximum duration of a single observation; , It is a potential spatial collection of aerial targets. It represents the total potential space for aerial targets; It is the potential space for aerial targets. The benefits; It is the potential space for aerial targets. Discrete 3D mesh set; It is the potential space for aerial targets. The proportion of the total number of observation grids to the total number of discrete 3D grids; It is the weight of the task's rate of return; It is the weight of the energy surplus rate; , It is the potential space for aerial targets. In satellite track Inner One observation band, It is the start time of the observation. It is the end time of the observation. It is the lateral sway angle of the observation strip. It is the set of three-dimensional grids covered by the strip; , It is the potential space for aerial targets. In satellite track The set of observation strips within, The number of its observed bands; It is a satellite The set of conflicting pairs of the upper observation strip; Indicates the potential space of aerial targets In satellite track Inner Whether an observation strip is executed is determined by a 1 if executed, otherwise by a 0.

[0027] In step S2, satellite observation strips are generated using the 3D mesh vertex method based on relevant data from multi-satellite collaborative resources. The specific implementation process is as follows: Determine the satellite In orbital orbit Potential space for aerial targets Internal 3D mesh vertices Starting from the vertex, the field of view is expanded to one side, forming a corresponding angular interval; taking the visible moment of the vertex as the starting time, the time is expanded according to the maximum single observation duration, forming a corresponding time interval [visible moment, visible moment + maximum single observation duration], thereby determining the maximum coverage area; for space For all other vertices within the range, check their lateral tilt angle and visibility time to see if they fall within the maximum coverage area. Merge the time windows of vertices falling within the maximum coverage area to form a time window set, creating an observation strip. Specifically: satellite In orbital orbit Potential space for aerial targets Internal 3D mesh vertices Visible time window Since a vertex has only one location, its visible time window is... Including a visible moment and a side angle .like Figure 3 As shown, for vertices Visible time window and satellite Vertical rail field of view and maximum single observation duration ,use and This is used to express the angle and time range of maximum coverage of the satellite transit strip; then, potential air targets within this range are filtered based on their side sway angle and visible time. All vertices within the range form the set of the maximum coverage time windows. Finally, based on the vertex... Visible time window and coverage time window Any vertex time window ,form and Combination of time window sets Calculate the corresponding observation strips Satellite In orbital orbit Potential space for aerial targets Inner Each stripe, where the stripe start time End time Side swing angle Covering mesh set (Each 3D grid) It includes 8 vertices, and its vertex set is ).

[0028] In step S3, the dominant satellite observation strips are selected from the satellite observation strips. The specific implementation process is as follows: Potential space for aerial targets In satellite track The set of observation strips within Since there must be a difference in quality among the observation strips in the set, this invention proposes a dominance criterion for satellite observation strips. Based on the comparison of the time and coverage grid attributes of the observation strips, observation strips with poor coverage and long observation time are eliminated to form an effective set of candidate observation strips.

[0029] Observation strip dominance criterion: for If the observed band With observation strips If the former's coverage grid set contains the latter's coverage grid set, and the former's observation time range falls within the latter's observation time range, then the former is dominant over the latter. , and ,but This means that the former requires only a shorter duration but can observe more 3D grids, thus eliminating the dominant observation strips. .

[0030] In step S4, a three-dimensional spatial search mission planning model for airborne targets is constructed based on relevant data from multi-satellite collaborative resources and the dominant candidate observation strips. The specific implementation process is as follows: The planning model for a three-dimensional spatial search mission of aerial targets includes an objective function and constraints. The objective function aims to maximize mission profitability and energy surplus, and its expression is as follows: (1) In the formula, The number of grid unions corresponding to all selected observation strips divided by the potential space of airborne targets. The number of discrete 3D meshes is obtained; , For the reason The set of 3D grids corresponding to the defined observation strips. .

[0031] The constraints include: The observation time for each observation strip cannot exceed the maximum single observation duration of the satellite: (2) Two observation strips on the same satellite with time conflicts cannot be executed simultaneously: (3) The total energy consumption for observations within each satellite orbital revolution must not exceed the maximum energy consumption for that revolution. (4) (5) The range of values ​​for the decision variable: (6) In step S5, the three-dimensional spatial search mission planning model for airborne targets is solved to obtain the satellite scheduling scheme. The specific implementation process is as follows: This invention employs an improved simulated annealing algorithm to solve the planning model for a three-dimensional spatial search mission of airborne targets. Unlike traditional simulated annealing, this invention improves neighborhood generation, acceptance criteria, and taboo object design, integrating the global exploration capabilities of simulated annealing with the local optimization capabilities of taboo search to design an improved hybrid algorithm for solving the scheduling scheme. First, the algorithm initializes relevant input parameters, generates and verifies an initial optimal solution. Then, in the simulated annealing phase, a neighborhood solution is generated based on a "satellite-orbit-region" three-dimensional association strategy, and constraints are verified in real time. The optimal solution is updated through objective function value comparison and adaptive acceptance criteria, and the process is cyclically cooled until a termination temperature is reached. Subsequently, in the taboo search phase, candidate solutions are generated based on the optimal solution from simulated annealing, and the optimization direction is adjusted through a taboo table to further refine the optimal solution. Finally, the satellite execution plan is output based on the final globally optimal solution. The specific process is as follows: Figure 4 As shown below, in conjunction with Figure 4 The solution process is explained in detail: S501, Algorithm initialization, specifically including: Input optimal strip set The objective function and constraints of the mathematical model are defined. Parameters such as initial temperature, cooling coefficient, termination temperature, tabu list length, and tabu search iteration count are set. Initial solutions are randomly generated (each solution is a decision variable). (The combination of values); for each initial solution, verify the constraints of equations (2)-(4) one by one, and retain the solutions that satisfy all constraints; select the solution with the highest profit among the retained solutions as the current optimal solution. And set it as the global optimal solution. .

[0032] S502. Determine if the current temperature is greater than the termination temperature. If so, execute the simulated annealing phase, updating the optimal solution by comparing the objective function value and using an adaptive acceptance criterion until the current temperature is less than or equal to the termination temperature. Output the updated global optimal solution as the optimal solution for simulated annealing. The specific implementation process is as follows: Abandoning the inefficient neighborhood generation method of traditional random flipping, a strip flipping strategy with a three-dimensional association of "satellite-orbit-region" is designed. At the current temperature... At that time, Randomly select different "satellite-orbit-region" strips and flip their decision variables. (0→1 or 1→0), generate neighborhood solutions Constraint verification logic is embedded synchronously during the neighborhood solution generation process to verify the generated solution. The constraints of equations (2)-(4) are verified in real time. If any constraint is not satisfied, the equations are immediately regenerated until a constraint that satisfies the constraints is obtained. This avoids substituting invalid solutions into the objective function calculation process. Calculate separately. (Current objective function value) and (Neighborhood solution objective function value).

[0033] like Accept directly ,renew ; like We introduce a "difference coefficient between the objective function of the current solution and the global optimal solution". The acceptance probability will be adjusted as follows: Generate random numbers ,like Then accept ,renew .

[0034] like ,renew .

[0035] .

[0036] After the phase ends, output the optimal solution for simulated annealing. .

[0037] S503. Using the simulated annealing optimal solution output in S502 as the initial baseline solution, perform a tabu search to output the final global optimal solution. The specific implementation process is as follows: Contraindications list Empty, storing "strip inversion combinations" (identified as "satellites") of the most recent poor solutions. - Laps -area - strip The set of tabu combinations is used instead of the traditional "complete solution" to avoid the tabu range being too wide and thus blocking valid solutions. A fixed tabu length is set, and the earliest added combination is periodically removed. The optimal solution output during the simulated annealing phase is used. As the initial baseline solution for the tabu search, the strips with the highest objective function are preferentially retained. (No flipping), choose strip flipping with a low objective function. Generate neighborhood solutions that meet the constraints. Extract the strip flipped combination of each neighborhood solution; if not... Then the middle is included in the candidate solution set. ;calculate The objective function of each solution is used to select the solution with the highest profit. ;like , lift the ban Reverse combinations (if they exist), update and add combinations of other candidate solutions. ;like , directly Combination ;like Delete the earliest added combination.

[0038] After the phase ends, the final globally optimal solution is output. .

[0039] S504. Based on the stripes with execution status 1 in the final global optimal solution, organize and output the execution schemes for each satellite as the satellite scheduling scheme. Specifically, this includes: based on middle The stripes are organized and the execution plans for each satellite are output, including the satellite number. laps ,area strip Start time End time Side swing angle Coverage mesh set .

[0040] This invention also provides a multi-satellite collaborative resource scheduling system for airborne target space search missions, which is used to execute the multi-satellite collaborative resource scheduling method for airborne target space search missions as described above.

[0041] This invention also provides a computer-readable storage medium storing a computer program for multi-satellite cooperative resource scheduling for an airborne target space search mission, wherein the computer program causes a computer to execute the multi-satellite cooperative resource scheduling method for an airborne target space search mission as described above.

[0042] This invention also provides an electronic device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a multi-satellite cooperative resource scheduling method for performing an airborne target space search mission as described above.

[0043] In summary, compared with existing technologies, it has the following beneficial effects: 1. The embodiment of the present invention uses a three-dimensional mesh vertex method to generate satellite observation strips, which comprehensively considers the three-dimensional maneuvering characteristics of air targets, extends the search task from a two-dimensional plane to a three-dimensional space, and avoids direct calculation of complex spatial geometry. This enables the search to effectively cover targets at different altitude levels and solves the problem that traditional two-dimensional scheduling methods cannot reliably detect air targets because they ignore the altitude dimension.

[0044] 2. In response to the situation of numerous candidate observation strips and serious redundancy, the embodiments of the present invention construct a satellite observation strip dominance criterion to optimize the candidate observation strips, retaining observation strips with the same coverage effect and shorter observation duration, thereby reducing the complexity of the satellite resource scheduling problem and laying the foundation for efficient solution.

[0045] 3. The three-dimensional spatial search task planning model designed in this embodiment of the invention, which aims to maximize the mission yield and energy surplus rate, can guide the selection of more efficient observation strips, reduce spatial overlap and resource waste in multi-star collaborative observation, and incorporate multiple practical constraints such as the maximum single observation duration, observation strip conflicts, and upper limit of orbital energy, thus taking into account both the benefits and feasibility of the search scheme.

[0046] 4. The improved simulated annealing algorithm designed in this embodiment of the invention innovatively integrates the advantages of global wide-area traversal of simulated annealing with the local fine-grained optimization capability of tabu search. It is combined with a neighborhood generation mechanism based on the three-dimensional association of "satellite-orbit-region", real-time embedded constraint verification logic and dynamically adjusted adaptive acceptance criteria. At the same time, it incorporates a high-yield strip protection strategy and a precise tabu control mechanism. It breaks through the bottleneck of traditional algorithms from multiple dimensions such as architecture design, iteration efficiency and optimization accuracy. It avoids the problems of single algorithms being prone to getting trapped in local optima and slow convergence. At the same time, it reduces redundant calculations in the huge solution space, enhances the global exploration capability of the algorithm, and effectively improves the search performance of the algorithm in the huge solution space, thereby obtaining a better aerial target space search scheme.

[0047] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-satellite cooperative resource scheduling method for an aerial target space search task, characterized in that, include: Acquire relevant data on multi-satellite collaborative resources for space search missions targeting aerial targets; Based on relevant data from multi-satellite collaborative resources, satellite observation strips are generated using the 3D grid vertex method. Select the dominant satellite observation strips from the satellite observation strips; Based on the relevant data of multi-satellite collaborative resources and the dominant candidate observation strips, a three-dimensional spatial search mission planning model for airborne targets is constructed. The satellite scheduling scheme is obtained by solving the three-dimensional spatial search mission planning model for aerial targets; The method of generating satellite observation strips using a 3D mesh vertex method includes: determining satellite intra-orbit potential space of aerial targets stereoscopic grid vertexes from the vertex, expand the field of view angle to one side to form a corresponding angle interval; Starting with the visible time of the vertex, the time is extended according to the maximum single observation duration to form the corresponding time interval [visible time, visible time + maximum single observation duration], and the corresponding maximum coverage area is determined based on the time interval; space For all other vertices within the range, check their lateral angle and visibility time to see if they fall within the maximum coverage area. Merge the time windows of vertices that fall within the maximum coverage area to form a time window set, thus creating a potential space for aerial targets. In satellite track The observation strips within.

2. The multi-satellite cooperative resource scheduling method for space search missions of airborne targets as described in claim 1, characterized in that, The process of selecting the dominant satellite observation strips from the satellite observation strips includes: against ,in, It is the potential space for aerial targets. In satellite track The set of observation strips within, ; If the observed strip With observation strips If the former's coverage grid set contains the latter's coverage grid set, and the former's observation time range falls within the latter's observation time range, then the former is dominant over the latter. Remove the dominant observation bands .

3. The multi-satellite cooperative resource scheduling method for space search missions of airborne targets as described in claim 1, characterized in that, The planning model for the three-dimensional spatial search mission of aerial targets includes an objective function and constraints. The objective function aims to maximize both the task profitability and the energy surplus rate, and its expression is as follows: (1) In the formula, The constraints include equations (2), (3), and (4): The observation time for each observation strip cannot exceed the maximum single observation duration of the satellite: (2) Two observation strips on the same satellite with time conflicts cannot be executed simultaneously: (3) The total energy consumption for observations within each satellite orbital revolution must not exceed the maximum energy consumption for that revolution. (4) (5) The range of values ​​for the decision variable: (6) in, For the reason The set of three-dimensional grids corresponding to a given observation strip; , It is a collection of satellites. It is the total number of satellites; , It is a satellite The set of orbital cycles within a scheduling period. For satellites within the scheduling cycle Total number of laps; satellite Maximum energy within a single orbital cycle; satellite The lateral sway velocity; satellite The energy consumed per unit observation time; satellite The maximum duration of a single observation; , It is a potential spatial collection of aerial targets. It represents the total potential space for aerial targets; It is the potential space for aerial targets. The benefits; It is the potential space for aerial targets. Discrete 3D mesh set; Potential space for aerial targets The proportion of the total number of observation grids to the total number of discrete 3D grids; It is the weight of the task's rate of return; It is the weight of the energy surplus rate; , It is the potential space for aerial targets. In satellite track Inner One observation band, It is the start time of the observation. It is the end time of the observation. It is the lateral sway angle of the observation strip. It is the set of three-dimensional grids covered by the strip; , It is the potential space for aerial targets. In satellite track The set of observation strips within, The number of its observed bands; It is a satellite The set of conflicting pairs of the upper observation strip; Indicates the potential space of aerial targets In satellite track Inner Whether an observation strip is executed is determined by a 1 if executed, otherwise by a 0.

4. The multi-satellite cooperative resource scheduling method for space search missions targeting aerial targets as described in any one of claims 1 to 3, characterized in that, Solving the planning model for the three-dimensional spatial search mission of aerial targets includes: Set the algorithm input data and execution parameters, randomly generate an initial solution, and determine the current optimal solution based on the initial solution. As the globally optimal solution; where the input data includes the optimal strip set. The objective function and constraints of the three-dimensional spatial search mission planning model for aerial targets; execution parameters include initial temperature, cooling coefficient, termination temperature, taboo table length, and taboo search iteration number, etc. Determine if the current temperature is greater than the termination temperature. If so, execute the simulated annealing stage. Update the optimal solution by comparing the objective function value and the adaptive acceptance criterion until the current temperature is less than or equal to the termination temperature. Output the updated global optimal solution as the simulated annealing optimal solution. Using the optimal solution from simulated annealing as the initial baseline solution, a tabu search is performed to output the final global optimal solution; Based on the stripes with an execution state of 1 in the final global optimal solution, organize and output the execution schemes of each satellite as the satellite scheduling scheme.

5. The multi-satellite cooperative resource scheduling method for space search missions of airborne targets as described in claim 4, characterized in that, The simulated annealing phase includes: For the current optimal solution Randomly select different "satellite-orbit-region" strips and flip their decision variables. Generate neighborhood solutions Constraint verification logic is embedded synchronously during the neighborhood solution generation process to verify the generated solution. The system performs real-time checks to ensure that constraints are met. If a constraint is not met, the system is immediately regenerated until a result that meets the constraints is obtained. ; Calculate the current optimal solution respectively objective function value and neighborhood solutions objective function value ; like Accept directly ,renew ; like Set an acceptance probability P and generate random numbers. ,like Then accept ,renew ; like ,renew Otherwise, keep constant; Update current temperature , ; The cooling coefficient; After the phase ends, output the optimal solution for simulated annealing. .

6. The multi-satellite cooperative resource scheduling method for airborne target space search missions as described in claim 5, characterized in that, The calculation method for the acceptance probability P includes: in, This represents the coefficient of difference between the current solution and the objective function of the global optimal solution.

7. The multi-satellite cooperative resource scheduling method for space search missions of airborne targets as described in claim 4, characterized in that, The execution of tabu search includes: Initialize the taboo table Empty; The optimal solution output by the simulated annealing stage As the initial baseline solution for the tabu search, we prioritize retaining the strips with higher objective functions and select strips with lower objective functions for inversion. Generate neighborhood solutions that meet the constraints. ; Extract the strip flipped combination of each neighborhood solution; if not... Then the middle is included in the candidate solution set. ;calculate The objective function of each solution is used to select the solution with the highest profit. ; like , lift the ban The flipped combination, updated and add combinations of other candidate solutions. ; like , directly Combination ; like Delete the earliest added combination, among which, This is the maximum length of the taboo list; After the phase ends, output As the final globally optimal solution.

8. A multi-satellite collaborative resource scheduling system for space search missions targeting aerial targets, characterized in that, The multi-satellite collaborative resource scheduling system for air target space search missions is used to execute the multi-satellite collaborative resource scheduling method for air target space search missions as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, It stores a computer program for multi-satellite cooperative resource scheduling for space search missions for airborne targets, wherein the computer program causes the computer to execute the multi-satellite cooperative resource scheduling method for space search missions for airborne targets as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a multi-satellite cooperative resource scheduling method for performing an airborne target space search mission as described in any one of claims 1 to 7.

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